1. Technical Field
This disclosure generally relates to streaming applications, and more specifically relates to enhancing debugging of a streaming application using breakpoints for data tuples that stay in an operator graph too long.
2. Background Art
Streaming applications are known in the art, and typically include multiple operators coupled together in an operator graph that process streaming data in near real-time. An operator typically takes in streaming data in the form of data tuples, operates on the data tuples in some fashion, and outputs the processed data tuples to the next operator. Streaming applications are becoming more common due to the high performance that can be achieved from near real-time processing of streaming data.
Many streaming applications require significant computer resources, such as processors and memory, to provide the desired near real-time processing of data. However, the workload of a streaming application can vary greatly over time. Allocating on a permanent basis computer resources to a streaming application that would assure the streaming application would always function as desired (i.e., during peak demand) would mean many of those resources would sit idle when the streaming application is processing a workload significantly less than its maximum. Furthermore, what constitutes peak demand at one point in time can be exceeded as the usage of the streaming application increases. For a dedicated system that runs a streaming application, an increase in demand may require a corresponding increase in hardware resources to meet that demand.
Cloud-based streaming is known in the art. Known systems for cloud-based streaming do not monitor data tuples to determine when a tuple has been in an operator graph too long.